Item recognition method and apparatus

ABSTRACT

The invention provides in one form an item recognition apparatus for recognising an individual item from a variety of known products for purposes of entry into an inventory and/or register system, said apparatus comprising (a) an identification station having a viewplate with backside illumination to provide a backlit image viewable from the frontside of the viewplate, a digital image capture means arranged to view said backlit image, a display screen for viewing by an operator, and an entry means for the operator to selectively make entries into the inventory and/or register system; and (b) computer storage and processing means having a first memory store containing data representing a reference image of said viewplate, a second memory store containing a product database of data sets representing image descriptors characterising features of size and/or shape for all of said variety of known products, and digitised image processing and analysing means for processing and analysing a digitised image captured by said digital image capture means, including means for using the data in said first memory store to subtract the representation of said reference image, means for generating at least one image descriptor characterising features of size and/or shape of the item to be identified, and means for comparing said at least one image descriptor with said stored data in said second memory store to identify stored data sets with the highest correlation and to produce a recognition result, and means for communicating said recognition result to said display screen.  
     The invention allows recognition of items independently of other objects which may be present on the viewplate, and the system can be trained to recognise items which may be subsequently added to the variety of know products.  
     The invention has particular application in POS item recognition in hardware stores and the like.

[0001] The present invention relates to a method and apparatus for itemrecognition and, more specifically, to the automatic recognition ofitems at a “point of sale” (POS) in a retail outlet, such as at ahardware store checkout.

[0002] In all retail environments the various items of purchase need tobe identified for entry into a computerised inventory and registersystem. This is very often done by applying a UPC (or similar) barcodeto the item and then using the scanner at the POS to scan the barcode inorder to produce a unique data entry for passing to the computerisedsystem. This system is popular and effective and used now in all typesof retail outlets. However, in the case of some items this approach issimply unrealistic, as applying a bar code to the item can beimpractical. For example, in a hardware store a shopper may bepurchasing small, loose items such as nuts, bolts and other fasteners.It may not be cost-effective or it may not be possible or convenient toreliably apply a bar code to such items, and the present approach toaddressing this problem is to leave the job of item identification tothe checkout operators, each checkout being provided with a manuallook-up directory whose pages display images of each item sorted bycategory and presented alongside an individual barcode. The operatorwill look up the item image in the relevant category and, once it hasbeen identified, will simply scan the accompanying barcode to enter theitem into the computerised system. Whilst this system is generallyfairly reliable it is naturally time-consuming and inefficient, andrelies on an accurate categorisation of the various items and continuousupdates to the manual directories. Thought has been given tocomputerising this procedure, and displaying the directory on a screenfor enquiry by the checkout operator, but this approach does little torelieve the basic inefficiency of such an identification procedure atthe POS.

[0003] Previous attempts have been made at providing a POS checkoutsystem which automates item identification. In U.S. Pat. No. 5,497,314,for an “Automated Apparatus and Method for Object Recognition atCheckout Counters”, an unmanned checkout station is disclosed to whichthe items to be processed are conveyed by any appropriate means, theitems being placed on the conveyor by the customer and automaticallyconveyed into an enclosed housing containing an illumination source,weight differentiation means and video imaging equipment. Sensors areused to control the operation of the automatic conveyor, and digitalimage processing techniques are then used to identify the items bytaking a digitised image of the object at a known focal point andillumination, and correlating it by way of “template matching” to theset of digitised images contained within the database of the system.

[0004] The system of U.S. Pat. No. 5,49,314 uses 3-D images withcontrolled overhead lighting, and requires a housing to minimise theeffect of extraneous light sources. Since the items to be identified aremoving on a conveyor belt, the system also requires either a camera lenswith auto-focus capability or a location sensor to determine theposition of the various items on the belt, to ensure they are in thefocal plane of the camera lens when the digitised image is captured.Whilst sophisticated in its complexity the system disclosed isinherently slow and cumbersome and its cost likely to be too high to becompetitive in most retail markets.

[0005] The present invention aims to address the drawbacks of the priorart, and to that end there is provided a method for recognising anindividual item at an identification station from a variety of knownproducts for purposes of entry into an inventory and/or register system,said method comprising the steps of;

[0006] providing a viewplate at said identification station;

[0007] illuminating said viewplate from the backside to provide abacklit image viewable from the front side of the viewplate;

[0008] providing a digital image capturing means positioned relative tothe viewplate to capture said backlit image;

[0009] capturing and storing a reference image of said viewplate;

[0010] supporting the item to be identified adjacent the viewplate;

[0011] viewing said item with said digital image capturing means tocapture said backlit image;

[0012] providing a digitised image signal representing said capturedimage;

[0013] processing said digitised image signal by subtraction of arepresentation of said stored reference image to remove viewplateartefact, to provide a background-compensated image signal;

[0014] image-analysing said background-compensated image signal togenerate at least one image descriptor characterising features of sizeand/or shape of the item to be identified;

[0015] providing a computerised product database for storage of datasets representing image descriptors characterising features of sizeand/or shape for all of said variety of known products;

[0016] comparing said at least one item image descriptor with the datastored on said computerised product database to identify data sets withthe highest correlation;

[0017] displaying a recognition result, being a visual representation ofat least one product corresponding to the data sets with the highestcorrelation; and

[0018] providing means for selective verification whereby an operatorcan confirm the recognition result for entry into the inventory and/orregister system.

[0019] In a preferred form, the viewplate is arranged for supporting theitem to be identified, the backside of the viewplate then being theunderside, and the digital image capturing means being arranged abovethe viewplate to capture the backlit image.

[0020] Preferably, said digital image capturing means is ablack-and-white digital camera and said digitised image signal is agrey-scale image signal. For an additional level of item discrimination,the digital image capturing means may comprise a colour camera.

[0021] In one form of the invention, said identification station is acheckout counter in a retail goods outlet, provided with a displayscreen for displaying said recognition result.

[0022] In a preferred form, said viewplate is associated with a weightdata means capturing weight data of said item to be identified, and saidweight data is also used in said computerised product database and saidcomparison step to produce the recognition result.

[0023] Preferably, said digitised image signal or said backgroundcompensated image signal is subjected to a binary threshold step, andadditionally or alternatively said background compensated image signalis subjected to a filtering step to reduce or remove noise and/orerrors.

[0024] Said image analysing step may include an object segmentation stepwhich serves to isolate a segmented image of an individual item from anyother parts of the image. The object segmentation step may involve aselection based on prescribed criteria of size, shape and/or position toselect a segmented image of an individual item from other segmentedimages.

[0025] In a preferred embodiment, said generation of said at least oneimage descriptor involves calculating a representation of at least oneparameter of said background-compensated image signal selected from thegroup of width, length, area, occupation ratio, circularity, perimeter,eccentricity, hole number, hole size, boundary feature, radial diameterssignature, minimum radial diameters, maximum radial diameters, mean ofradial diameters, standard deviation of radial diameters, radial radiisignature, minimum radial radii, maximum radial radii, mean of radialradii, standard deviation of radial radii, first Hu moment, second Humoment, third Hu moment, fourth Hu moment, fifth Hu moment, sixth Humoment and seventh Hu moment.

[0026] Preferably, said comparison step involves a pattern recognitionalgorithm, such as a K-Nearest Neighbour technique.

[0027] Said recognition result may be a candidate list of a number ofsaid products with the data sets having the highest ranking correlation.

[0028] The visual representation may be an image of a product orproducts stored on the computerised product database, and both thedisplaying step and the step of selective verification are preferablycarried out at the identification station. The display step may includethe display of a barcode on said screen for selective scanning by theoperator. Alternatively, the display step may involve display on atouchscreen for product selection by the operator.

[0029] Preferably, said capturing and storing of the reference image isrepeated at intervals.

[0030] In another aspect, the invention provides an item recognitionapparatus for recognising an individual item from a variety of knownproducts for purposes of entry into an inventory and/or register system,said apparatus comprising:

[0031] an identification station having:

[0032] a viewplate with backside illumination to provide a backlit imageviewable from the frontside of the viewplate;

[0033] a digital image capture means arranged to view said backlitimage;

[0034] a display screen for viewing by an operator; and

[0035] an entry means for the operator to selectively make entries intothe inventory and/or register system;

[0036] computer storage and processing means having:

[0037] a first memory store containing data representing a referenceimage of said viewplate;

[0038] a second memory store containing a product database of data setsrepresenting image descriptors characterising features of size and/orshape for all of said variety of known products; and

[0039] digitised image processing and analysing means for processing andanalysing a digitised image captured by said digital image capturemeans, including:

[0040] means for using the data in said first memory store to subtractthe representation of said reference image;

[0041] means for generating at least one image descriptor characterisingfeatures of size and/or shape of the item to be identified; and

[0042] means for comparing said at least one image descriptor with saidstored data in said second memory store to identify stored data setswith the highest correlation and to produce a recognition result;

[0043] and means for communicating said recognition result to saiddisplay screen.

[0044] In a preferred form, said entry means is provided by atouchscreen. In an alternative form, the entry means may be a barcodescanner.

[0045] The apparatus may include a plurality of identification stations,the computer storage and processing means having a first memory storeassociated with each identification station and a central second memorystore for communication with each of the plurality of identificationstations.

[0046] Said identification station may include a weighscale associatedwith said viewplate, said product database including data characterisingthe weight of all of said variety of known products, and said comparisonmeans including means to compare weight data from an item with storedweight data in the product database.

[0047] The invention also provides a checkout station in a retail goodsoutlet incorporating the above-defined item recognition apparatus.

[0048] Also within the contemplation of the invention is a systemincluding the above-defined item recognition apparatus in combinationwith an inventory and/or register system.

[0049] To more clearly illustrate the invention, an exemplification willnow be described with reference to the accompanying drawings, in which:

[0050]FIG. 1 shows a schematic representation of an item recognitionviewplate at a POS checkout station, on which a typical item is placed;

[0051]FIG. 2 is a block diagram illustrating an overview of the softwaresystem performing the recognition function;

[0052]FIG. 3 is a block diagram illustrating the image processing stageof the system;

[0053]FIG. 4 is a block diagram illustrating the analysis stage of thesystem;

[0054]FIG. 5 is a block diagram illustrating the recognition stage ofthe system;

[0055]FIG. 6 is an overall view of the POS checkout station as seen by acheckout operator;

[0056]FIG. 7 shows a state transition diagram for an operator interface;and

[0057]FIG. 8 shows an example view of the operator interface.

[0058] At the POS checkout station a planar viewplate 10 is horizontallyarranged, upon which viewplate a checkout operator (or customer) placesan item to be recognised 11, such as a bolt. Viewplate 10 is mouldedwith a shallow raised edge to stop items from rolling off and is formedfrom white acrylic sheet or some other suitable light-diffusingmaterial. Viewplate 10 is illuminated from below by one or morefluorescent tubes 12 (although any other suitable light source may beused), to provide backlit illumination rendering a shadow or silhouetteimage to a viewer above viewplate 10. A black-and-white digital imagingcamera 13 with a resolution of 800×600 pixels is mounted approximately60cm above viewplate 10 and focussed thereon, to capture a 2-D imagewhen item 11 is placed on the viewplate, and thus within the focal planeof camera 13. An IR filter together with a UV filter is applied over thelens of camera to reduce the effect of external lighting on the system.It should be noted that viewplate may be incorporated in a wide varietyof checkouts or other locations in retail outlets, or at differentpoints in a warehouse or distribution station.

[0059] Camera 13 is connected to a software system 20 performing theitem recognition function, which comprises three main stages; imageprocessing 30; image analysis 40; and recognition 50. The othercomponents illustrated in FIG. 2 are a background image store 21 and aparts database 22, the purpose and operation of both components beingexplained in greater detail below. The output of the recognition systemis passed to a checkout display 23. Additionally a signal from weightdata means 24, such as a scale associated with viewplate 10, may beprovided to the recognition system for additional discriminationinformation.

[0060] Image Processing Stage 30 (FIG. 3)

[0061] At image capture step 31 the 2D shadow image captured as adigital representation is first converted (if necessary) to a formsuitable for processing as a grey-scale image. The next step isbackground removal 32, in which the system compensates for imageartefact or noise due to scratches or other marks present on viewplate10. For this calibration, black-and-white camera 13 regularly captures areference image when no items are present for calibration, and therebycontinually updates a background image store 21. Background removal step32 subtracts the reference image from the grey-scale image to remove thebackground artefact from the image. The resultant data then passesthrough a binary threshold step 33, which operates to convert thegrey-scale image to a binary image wherein each pixel is associated witha “1” or a “0”, depending on whether the item was or was not present inthe object area corresponding to that pixel. The final step in imageprocessing stage 30 is image filtering 34 in order to remove noise anderrors associated with the background removal and binary thresholdoperations. This image filtering step involves a standard smoothingoperation “opening” or “closing” image pixels according to an algorithmwhich takes into account the status of neighbouring pixels. Such imageprocessing methods are well known to the skilled reader and will not befurther described here. The output from this step 34 is a filteredbinary image 35.

[0062] Analysis Stage 40 (FIG. 4)

[0063] The aim of this stage is to determine characteristic featureinformation from the binary image 35 outputted from image processingstage 30. The first step here is object segmentation 41, enabling thesystem to handle more than a single item at once. This involvesisolating the image associated with each item 11 from the rest of theimage using a so-called ‘region-growing’ algorithm, and labelling thatsegmented image. Object segmentation of image data is well known to theskilled reader and will not be further described here. The next step isobject selection 42, in which the system makes a selection from thesegmented images of the most likely one required for recognition basedon size, shape and position criteria. The selected object is thenanalysed at object analysis step 43 and a large number of topologicaland structural features are generated, these features becoming the basisof a descriptive feature vector 44 that is used to uniquely identifyeach product.

[0064] The features generated include, but are not limited to, thoseappearing in Table 1. TABLE 1 Descriptors Image Feature Comment WidthLength perpendicular to width Area a summation of the number of “1”pixels Occupation Ratio within a rectangular bounding box CircularityPerimeter a summation of the edge pixels of the image EccentricityNumber of “Holes” a standard washer, say, would have one “hole” Size of“Holes” Boundary description features based on encoding vectorsgenerated around the edge of the image Radial diameters signature theradial diameters at regular angular spacings Minimum Radial diametersall meeting at the centre of gravity of the image Maximum Radialdiameters Mean of Radial diameters Standard deviation of Radialdiameters Radial radii signature similar to the radial diameterfeatures, but Minimum Radial radii producing distinction fornon-symmetrical items Maximum Radial radii Mean of Radial radii Standarddeviation of Radial radii First Hu Moment Standard area moments SecondHu Moment Third Hu Moment Fourth Hu Moment Fifth Hu Moment Sixth HuMoment Seventh Hu Moment

[0065] Feature vector 44 represents a weighted combination of thesedescriptors, the weighting being selected according to the particularapplication. It is to be noted that all these features areorientation-neutral, and it is therefore not necessary for the imageprocessing to attempt to rotate the image before analysis step 43.

[0066] Item Recognition Stage 50 (FIG. 5)

[0067] The next stage utilises a combination of artificial intelligenceand machine learning in a recognition step 51, in order to determine themost likely product matches based on descriptive feature vector 44, bycorrelation with the product data stored on parts database 22. Therecognition step involves a pattern recognition technique, and thepreferred technique is the K-Nearest Neighbour algorithm, since it isrelatively fast to train. The K-Nearest Neighbour algorithm per se iswell known to the skilled reader and will not be further described here.Other approaches which may be more appropriate in particularapplications are statistical techniques (Euclidean and other distancemeasures), combinations of Neural network and Fuzzy classificationtechniques, and search tree pruning techniques. These approaches aregenerally known in the field of pattern recognition, and the actualtechnique selected is likely to depend on the relative speeds oftraining and running the algorithms, as well as on the performance ofthose algorithms. If frequent updates to the product data are needed,then a technique which is fast to train is likely to be more appropriatethan one that may be fast to run but comparatively slow to train.

[0068] The product data on parts database 22 is encoded under equivalentconditions to those prevailing at the POS. The recognition step resultsin a list of candidates, and a results sorting step 52 then generates aproduct candidate list, which may be, say, a top-5 or top-15 list.Finally these candidates are passed to a display system 23 forappropriate display of a visual representation of the products in thecandidate list to a checkout operator, the visual representation being asample image of each product, a description and an associated barcodefrom parts database 12.

[0069] An additional parameter to enhance product discrimination and/orto speed up the recognition process is the item weight value, which datamay be captured by means of weight data means 24 such as a scaleassociated with viewplate 10. The weight of an item is a physicalattribute that can vary widely from product to product and thereforeprovide a very valuable recognition parameter, and minimise the errorrate, in situations where size and shape may vary between some differentitems to a minimal extent. The weight data is passed to recognitionstage 50 and used as part of the correlation data against weight datastored in product database 22 in generating the product candidate list.Clearly other physical parameters may additionally or alternatively beused in the appropriate circumstances to enhance the discriminationprocess, such as additional visual data (eg laser scanning or additionalcamera views from different angles), magnetic properties, acousticcharacteristics, spectral fingerprint, etc.

[0070] If the system fails to match the item on the viewplate with anypart from the product database (either because the part is not in thedatabase or due to problems arising from placement, lighting orcalibration), the operator can be given the option of cancelling theentire operation and reverting to a simple manual or on-screen productcatalogue.

[0071] The item recognition system is embodied in a software programmeon a PC or other local computer system, which may be linked to acentralised parts database 22, as part of a network of stations.Alternatively parts database 22 may be local to the checkout station andupdated regularly by network download or by means of a CDROM.

[0072] In use (see FIG. 6), the item 11 to be identified is placed onviewplate 10, which automatically triggers the image capture,processing, analysis and recognition steps as well as, if appropriate, aweight-value capture step. The resulting candidate list generated isthen displayed on display 23. The customer or checkout operator thenvisually verifies the correct match presented from those displayed, andmay have the operation of scrolling through pages of further candidatesif the correct result is not found on the first page. The operator thenuses a conventional hand-held checkout scanner to scan the associatedbarcode appearing on the display, or it may be preferred to circumventthe use of the barcode scanner by providing, say, a cursor- ortouchscreen-operated selection system to allow the operator to enter theselected product directly on the display screen. The system mayalternatively automatically send an identification to the computerisedinventory/register system (such as the UPC number of a product) when thecorrelation between an item and a particular product profile stored onparts database 22 exceeds a certain “definite match” threshold. Howeverit is thought that, since the checkout operator will be using a barcodescanner to enter other, larger items, for which the present inventionmight not be appropriate, then the use of screen barcodes will be moreconvenient and avoid undesirable interruption in the entry of a basketof diverse items. Furthermore there may be items of indeterminatetopography without barcodes (such as cut lengths of rope in a hardwarestore) which will need individual attention and selection from anon-screen product catalogue by the checkout operator.

[0073] It has been found to be preferable in many situations to use aninfra-red light source 12 (such as an array of IR-LEDs), detectable bycamera 13, in order to minimise the effect of extraneous light sourceson the image capture step. This is particularly the case when theobjects to be recognised may be metallic, and therefore difficult toilluminate without introducing unwanted reflection.

[0074] In addition to the type of light source 12 employed, the lightintensity emitted by the source has to be optimised. The selected sourceintensity is a factor of the viewplate properties and dimensions, theform of illumination used, the distance between the light source andviewplate, the camera lens filter used, as well as the sensitivity ofthe camera and image capture equipment. Ideally a small aperture is usedfor camera 13 in order to reduce the sensitivity to the effect ofexternal light sources, and also to increase the depth of field, andtherefore a higher intensity of illumination is generally preferred.

[0075] In a system tested (see below), an optimal distance between anIR-LED board and the viewplate, required for a clear and uniformbackground image, was found to be 8 cm.

[0076] In a form of the invention tested by the inventors, atouch-enabled colour 800×600 LCD panel (touchscreen) is used as display23, for both viewing item 11, for displaying the resulting productcandidate list, and for operator entry of system commands and productselection. The interface transition diagram of FIG. 7 diagrammaticallyillustrates the use of this panel, whilst the sample panel display shownin FIG. 8 shows a page of a resulting candidate list as well as theavailable operator commands.

[0077] A single item recognition apparatus is arranged to support twoneighbouring cash registers, one on either side of the apparatus, and anoperator can decide to which cash register a selected productidentification (eg. a machine barcode) is sent by making a selectioneither on the right hand side or the left hand side of the displayscreen (see below).

[0078] To begin use of the apparatus on a new item, the operator firstclears viewplate 10 and touches screen 23. This switches display 23 toprovide a live video feed from camera 13. The operator places item 11 tobe identified on viewplate 10, ensuring the part is stationary and theimage appears completely within the boundaries of the screen display.The operator then has the choice of selecting a MENU option by touchinga part of the touchscreen (in which case a main menu is displayedallowing the operator to choose further options or to shut down thesystem), or to touch any other part of the image screen to begin theitem recognition process.

[0079] After a short delay, the touchscreen display 23 then displays anew screen (FIG. 8) including the product candidate list and selectablescreen buttons. The candidates 60 are displayed as colour high-qualityimages together with a product description for each image, in descendingorder of likelihood in accordance with the result of applying thecomputer pattern recognition algorithm. If the correct result is notfound on the first page, the operator may press button 64 to scrollforward through further pages, the page number being displayed in acorner of the screen 63. On the final page a CATALOGUE button will beavailable, enabling the operator to switch to a catalogue display if thecorrect product does not appear amongst candidates 60. If the systemfails to match item 11 for any reason, the operator can press CANCELbutton 64 to cancel the operation and try again, or alternatively switchto the CATALOGUE option.

[0080] When the operator successfully locates the correct product fromcandidates 60, the desired selection is made by pressing on theappropriate button 61 or 62, depending on whether the selection is madefor an entry in the right hand register or the left hand cash register.Once the selection is made, item 11 is then removed from viewplate andtouchscreen 23 is touched once more to return to the live video feedsignal display.

[0081] It is to be noted that the system may be set up to recognisemultiple items placed on viewplate 10,by applying the analysis andrecognition steps to each of a number of segmented images identifiedfrom a single captured image.

[0082] It is understood that various modifications, alterations and/oradditions may be made to the embodiments specifically described andillustrated herein without departing from the spirit and scope of theinvention.

1. A method for recognising an individual item at an identificationstation from a variety of known products for purposes of entry into aninventory and/or register system, said method comprising the steps of;providing a viewplate at said identification station; illuminating saidviewplate from the backside to provide a backlit image viewable from thefront side of the viewplate; providing a digital image capturing meanspositioned relative to the viewplate to capture said backlit image;capturing and storing a reference image of said viewplate; supportingthe item to be identified adjacent the viewplate; viewing said item withsaid digital image capturing means to capture said backlit image;providing a digitised image signal representing said captured image;processing said digitised image signal by subtraction of arepresentation of said stored reference image to remove viewplateartefact, to provide a background-compensated image signal;image-analysing said background-compensated image signal to generate atleast one image descriptor characterising features of size and/or shapeof the item to be identified; providing a computerised product databasefor storage of data sets representing image descriptors characterisingfeatures of size and/or shape for all of said variety of known products;comparing said at least one item image descriptor with the data storedon said computerised product database to identify data sets with thehighest correlation; displaying a recognition result, being a visualrepresentation of at least one product corresponding to the data setswith the highest correlation; and providing means for selectiveverification whereby an operator can confirm the recognition result forentry into the inventory and/or register system.
 2. A method accordingto claim 1, wherein said digital image capturing means is ablack-and-white digital camera and said digitised image signal is agrey-scale image signal.
 3. A method according to claim 1 or claim 2,wherein said identification station is a checkout counter in a retailgoods outlet, provided with a display screen for displaying saidrecognition result.
 4. A method according to any preceding claim,wherein said viewplate is associated with a weight data means capturingweight data of said item to be identified, and said weight data is alsoused in said computerised product database and said comparison step toproduce the recognition result.
 5. A method according to any precedingclaim, wherein said digitised image signal or said backgroundcompensated image signal is subjected to a binary threshold step.
 6. Amethod according to any preceding claim, wherein said backgroundcompensated image signal is subjected to a filtering step to reduce orremove noise and/or errors.
 7. A method according to any precedingclaim, wherein said image analysing step includes an object segmentationstep which serves to isolate a segmented image of an individual itemfrom any other parts of the image.
 8. A method according to claim 7,wherein the object segmentation step involves a selection based onprescribed criteria of size, shape and/or position to select a segmentedimage of an individual item from other segmented images.
 9. A methodaccording to any preceding claim, wherein said generation of said atleast one image descriptor involves calculating a representation of atleast one parameter of said background-compensated image signal selectedfrom the group of: width: length; area; occupation ratio; circularity;perimeter; eccentricity; hole number; hole size; boundary feature;radial diameters signature; minimum radial diameters; maximum radialdiameters; mean of radial diameters; standard deviation of radialdiameters; radial radii signature; minimum radial radii; maximum radialradii; mean of radial radii; standard deviation of radial radii; firstHu moment; second Hu moment; third Hu moment; fourth Hu moment; fifth Humoment; sixth Hu moment; and seventh Hu moment.
 10. A method accordingto any preceding claim, wherein said comparison step involves a patternrecognition algorithm.
 11. A method according to claim 10, wherein thepattern recognition algorithm involves a K-Nearest Neighbour technique.12. A method according to any preceding claim, wherein said recognitionresult is a candidate list of a number of said products with the datasets having the highest ranking correlation.
 13. A method according toany preceding claim, wherein the visual representation includes an imageof a product or products stored on said computerised product database.14. A method according to any preceding claim, wherein the display stepincludes the display on a touchscreen for product selection by theoperator.
 15. A method according to any preceding claim, wherein saidcapturing and storing of the reference image is repeated at intervals.16. An item recognition apparatus for recognising an individual itemfrom a variety of known products for purposes of entry into an inventoryand/or register system, said apparatus comprising: an identificationstation having: a viewplate with backside illumination to provide abacklit image viewable from the frontside of the viewplate; a digitalimage capture means arranged to view said backlit image; a displayscreen for viewing by an operator; and an entry means for the operatorto selectively make entries into the inventory and/or register system;computer storage and processing means having: a first memory storecontaining data representing a reference image of said viewplate; asecond memory store containing a product database of data setsrepresenting image descriptors characterising features of size and/orshape for all of said variety of known products; and digitised imageprocessing and analysing means for processing and analysing a digitisedimage captured by said digital image capture means, including: means forusing the data in said first memory store to subtract the representationof said reference image; means for generating at least one imagedescriptor characterising features of size and/or shape of the item tobe identified; and means for comparing said at least one imagedescriptor with said stored data in said second memory store to identifystored data sets with the highest correlation and to produce arecognition result; and means for communicating said recognition resultto said display screen.
 17. An apparatus according to any precedingclaim, wherein said entry means includes a touchscreen.
 18. An apparatusaccording to claim 17, including a plurality of identification stations,the computer storage and processing means having a first memory storeassociated with each identification station and a central second memorystore for communication with each of the plurality of identificationstations.
 19. An apparatus according to claim 17 or claim 18, whereinsaid identification station includes a weighscale associated with saidviewplate, said product database including data characterising theweight of all of said variety of known products, and said comparisonmeans including means to compare weight data from an item with storedweight data in the product database.
 20. A checkout station in a retailgoods outlet incorporating an item recognition apparatus according toany one of claims 17 to
 19. 21. A system including the item recognitionapparatus of any one of claims 17 to 19 in combination with an inventoryand/or register system.
 22. A method substantially as herein describedwith reference to the accompanying drawings.
 23. An apparatussubstantially as herein described with reference to the accompanyingdrawings.